The following relates to the imaging, photographic, photofinishing, image enhancement, and related arts. The following is generally applicable to digital images of substantially any type and to devices for acquiring same, such as cameras, camcorders, web cameras, x-ray or other radiographic imagers, and so forth. The following is further generally related to images generated by photography, optical scanning of hardcopies, images generated by virtual image generation systems, and so forth. The following is generally applicable to two-dimensional images, three-dimensional images, or higher dimensional images. The following is generally applicable to color images, gray scale images, radiographic images, and so forth.
Digital imaging systems such as digital cameras, camcorders, web cameras, and so forth directly generate digital images. For example, a digital camera acquires an image using a charge-coupled device (CCD) array or other photodetector array. Film-based imaging systems such as 35-mm cameras, x-ray systems that use x-ray photographic plates, and so forth also can generate digital images, albeit indirectly, for example by optically scanning the film image. As images in digital form proliferate, there is increasing interest in applying photofinishing or image enhancement techniques to improve image characteristics such as hue, contrast, and so forth.
In the area of contrast enhancement, global and local techniques are known. Local approaches can provide effective contrast enhancement, are computationally intensive. Local approaches can also introduce artifacts, such as transition or boundary artifacts at the boundaries of different local contrast enhancement regions.
In a typical global technique, the intensities of pixels are adjusted based on a tone reproduction curve (TRC) that is expected to produce contrast enhancement. The gamma correction is one such generally contrast enhancing curve. A disadvantage of these approaches is that the selected TRC may provide little or no contrast enhancement for certain images, or may produce quality degradation such as a washed out image or a noise-enhanced image. In some image processing systems, the user can manually adjust the TRC for an image. This approach can provide good contrast enhancement, but depends on the skill of the operator and cannot be automated.
Automated global contrast enhancement based on content of the image has frequently utilized intensity histogram information. In a histogram equalization technique, a histogram of the intensity levels of pixels of an image is generated, and a TRC is selected and applied that generally equalizes the levels across the histogram. Empirically, such approaches have been found to provide some contrast enhancement, and are readily automated. Histogram equalization approaches can be justified in a non-rigorous manner by recognizing that the equalized histogram fully utilizes the grayscale range. However, enhancement by histogram equalization can degrade image quality. Equalization can produce excessive intensity level variation in dominant dark or dominant light regions of the image. When these regions contain noise such as sensor noise or compression artifacts, the result is that the noisy response is enhanced in the output image.